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A high efficient next generation reservoir computing to predict and generate chaos with application for secure communication

Leisheng Jin Orcid Logo, Zhuo Liu, Ai Guan, Zhen Wang, Rui Xue, Lijie Li Orcid Logo

IET Communications, Volume: 17, Issue: 4

Swansea University Author: Lijie Li Orcid Logo

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DOI (Published version): 10.1049/cmu2.12559

Abstract

In this work, a high efficient next generation reservoir computing (HENG-RC) paradigm that adopts the principle of local states correlation and attention mechanism is proposed, which is able to process dynamical information generated by both the low dimensional and very large spatiotemporal chaotic...

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Published in: IET Communications
ISSN: 1751-8628 1751-8636
Published: Institution of Engineering and Technology (IET) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa62214
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Abstract: In this work, a high efficient next generation reservoir computing (HENG-RC) paradigm that adopts the principle of local states correlation and attention mechanism is proposed, which is able to process dynamical information generated by both the low dimensional and very large spatiotemporal chaotic systems (VLSCS). From a dynamical system perspective, the dynamical characteristics such as density distribution, Poincaré plots and max Lyapunov exponents of the proposed HENG-RC are studied. It is revealed that the trained model can be seen as a data-driven chaotic system. Furthermore, a novel scheme of secure communication based on chaotic synchronization of two HENG-RC systems is designed, of which the security is enhanced as the intruder needs to know simultaneously the training signal and details of the parameter setting in the HENG-RC. The digital implementation using field programmable gate array is experimentally realised, proving the feasibility of the secure communication scheme.
Keywords: chaos synchronisation; data-driven; next generation reservoir computing; secure communication; time series prediction
College: Faculty of Science and Engineering
Funders: China Postdoctoral Science Foundation. Grant Number: 2019T120447
Issue: 4

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